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Analyzing where you’ve lost money on certain drugs but not sure who to ‘point the finger at’?

Looking at claims in a retail pharmacy’s “loss file,” PAC can be used to identify where the retail pharmacy should direct its attention:

Reimbursement Issue – Identify claims where reimbursement
(not including the dispensing fee) was less than PAClow.

Procurement Issue – Identify claims where the acquisition price from the wholesaler or manufacturer was more than PAChigh.

Use Case: A large regional retail chain (500+ pharmacies) leverages PAC to see if loss file issues are more likely due payer/PBM reimbursements versus its own procurement issues.

We compare each payment (less the dispensing fee) in the loss file to the PAClow and determine where there is a strong likelihood the payer/PBM has reimbursed at rate lower than what is acceptable. This is a conservative approach, looking only at the cost of the drug with no profit margin and comparing to the low end of our PAC range.

And we compare each drug acquisition price in the loss file to the PAChigh and determine where there is a strong likelihood the retailer purchased the drug above a price considered acceptable. Again, this is a conservative approach using the PAChigh and the retailer can be more aggressive.

Results – Based on this retailer’s loss file, 58% of claims were reimbursed below the published PAC which indicates that the payers/PBMs were more aggressive with their reimbursements. But the majority of these reimbursements did not fall below PAClow. Based on this conservative parameter (PAClow) for acceptable reimbursement, 15% of claims were reimbursed lower than PAClow.

At the same time, 23% of the claims (based on our conservative parameter of PAChigh) had a procurement price that was higher than acceptable. In these situations, it was not the reimbursement but rather that procurement price that resulted in these claims being added to the loss file.

PAClow and PAChigh

In addition to publishing the PAC, the PAClow and PAChigh are provided to subscribers. The PAC is an estimation of acquisition cost, but there can be a degree of uncertainty. PAClow and PAChigh establish a range within which there is a high degree of confidence that the true acquisition cost lies.

For a given pharmacy per-script profit target, this PAC range can translate into a range within which the MAC should lie:
In order to spot prime opportunities to create a more balanced MAC pricing, we can identify drug groups for which the current MAC is outside the range, i.e. MAClow to MAChigh.

SMAC Analysis

Scenario: Assuming a pharmacy profit-per-script target of at least $3/script and 10% margin (not including professional services/dispensing fee), the PAC range was used to identify drug groups where an increase or decrease of the SMAC may be appropriate.

SMAC

Raise

Reduce

Unchanged

State 1

609

210

991

State 2

282

1674

776

State 3

591

308

1603

State 4

425

133

668

State 4

361

133

514

State 5

132

546

183

State 6

747

177

925

State 7

212

202

299

State 8

965

506

1714

State 9

292

381

391

State 10

689

527

669

State 11

359

287

409

State 12

169

491

278

State 13

596

210

991

State 14

204

287

436

State 15

169

154

463

State 16

266

637

369

State 17

473

241

812

Overall

7541

7104

12491

We applied this MAClow and MAChighbased analysis to a sample of states where SMACs are available on the state Medicaid web sites. A count of drug groups with a SMAC falling outside recommended range is summarized in the table with the assumptions above.

We know specific business parameters exist when generating SMAC values and those parameters will vary from state to state. For example, the assumptions made regarding targeted pharmacy profitability can be adjusted and the incorporation of a pharmacy’s utilization can focus on the most relevant drug groups. Glass Box Analytics is available to provide a pharmacy with additional analytics that are tailored to that pharmacy’s areas of interests.

Scenario: A Medicaid manages its generic reimbursement at a generic effective rate (GER, i.e. the average percent discount off the AWP for all generic drugs) of 74%.

Let’s compare PAC Retail to AWP, each delivering an identical GER of 74% on the current MAC list given the state’s utilization across drug group.

Variability of GER across drug groups

AWP
Because AWP is so disconnected from acquisition cost, and hence from the Medicaid’s maximum allowable cost (MAC), the GER varies dramatically across drug groups when based on AWP. (A significant number of drugs even show a negative GER.)

PAC Retail
In contrast, looking across drug groups, the PAC Retail based GER is tightly centered around 74%.

The actual GER achieved is highly dependent on the utilization mix in the case of AWP, but much more predictable if based on PAC Retail.

Looking at NDC’s within a drug groupBeyond AWP’s disconnect with true acquisition cost, another issue exists when measuring the performance of a MAC using a GER metric based on AWP. When looking across NDC’s within a drug group, the AWP often varies even though the MAC is fixed at the drug group level.

The resulting GER therefore depends, in part, on which manufacturers a pharmacy purchases from (i.e. which NDCs within a drug group are utilized). This phenomenon, as illustrated below by a couple of examples, adds a further degree of uncertainty for the Medicaid when targeting GER-based performance metrics.

Drug Label

MAC

NDC

AWP

GER

TRETINOIN 0.05% CREAM

1.1860

45802036142

2.09511

43%

TRETINOIN 0.05% CREAM

1.1860

43478024220

2.51100

53%

HYDROCODON-ACETAMINOPHEN 5-500

0.0440

00406035705

0.19686

80%

HYDROCODON-ACETAMINOPHEN 5-500

0.0440

00591034901

0.50650

92%

In fact, the exact same drug group utilization could result in an AWP-based GER of anywhere from 68% to 77%, depending on which NDCs are actually involved.

PAC Retail, on the other hand, exhibits little to no variance across NDCs within a drug group and will deliver a stable PAC Retail-based GER fixed at 74%,.